Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.1 KiB
Average record size in memory391.7 B

Variable types

Numeric14
Text3
Categorical2
DateTime1

Alerts

acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
danceability is highly overall correlated with valenceHigh correlation
duration_minutes is highly overall correlated with duration_msHigh correlation
duration_ms is highly overall correlated with duration_minutesHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
explicit is highly overall correlated with speechinessHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
popularity is highly overall correlated with yearHigh correlation
speechiness is highly overall correlated with explicitHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
year is highly overall correlated with popularityHigh correlation
artists has unique values Unique
duration_ms has unique values Unique
duration_minutes has unique values Unique
energy has unique values Unique
id has unique values Unique
loudness has unique values Unique
name has unique values Unique
tempo has unique values Unique
valence has unique values Unique
instrumentalness has 16 (32.0%) zeros Zeros
key has 2 (4.0%) zeros Zeros

Reproduction

Analysis started2025-05-05 04:31:43.448309
Analysis finished2025-05-05 04:32:40.015720
Duration56.57 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

acousticness
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21789345
Minimum4.62 × 10-6
Maximum0.939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:40.342933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.62 × 10-6
5-th percentile0.0006639
Q10.0147
median0.0703
Q30.33475
95-th percentile0.8597
Maximum0.939
Range0.93899538
Interquartile range (IQR)0.32005

Descriptive statistics

Standard deviation0.28180953
Coefficient of variation (CV)1.2933364
Kurtosis0.59018611
Mean0.21789345
Median Absolute Deviation (MAD)0.068958
Skewness1.3370466
Sum10.894673
Variance0.079416611
MonotonicityNot monotonic
2025-05-05T05:32:40.785891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.866 2
 
4.0%
0.0253 2
 
4.0%
0.000402 1
 
2.0%
0.0471 1
 
2.0%
0.0985 1
 
2.0%
0.335 1
 
2.0%
0.00243 1
 
2.0%
0.163 1
 
2.0%
0.00393 1
 
2.0%
0.692 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
4.62 × 10-61
2.0%
0.000322 1
2.0%
0.000402 1
2.0%
0.000984 1
2.0%
0.0017 1
2.0%
0.00243 1
2.0%
0.00295 1
2.0%
0.00393 1
2.0%
0.00689 1
2.0%
0.00727 1
2.0%
ValueCountFrequency (%)
0.939 1
2.0%
0.866 2
4.0%
0.852 1
2.0%
0.779 1
2.0%
0.692 1
2.0%
0.65 1
2.0%
0.562 1
2.0%
0.445 1
2.0%
0.438 1
2.0%
0.428 1
2.0%

artists
Text

Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2025-05-05T05:32:41.689896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length36
Median length24
Mean length18.12
Min length10

Characters and Unicode

Total characters906
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row['Lil Wayne', 'Drake']
2nd row['blackbear']
3rd row['Lacuna Coil']
4th row['The Beach Boys']
5th row['In This Moment']
ValueCountFrequency (%)
the 5
 
4.2%
lil 2
 
1.7%
of 2
 
1.7%
sons 1
 
0.8%
1
 
0.8%
mumford 1
 
0.8%
ocean 1
 
0.8%
frank 1
 
0.8%
jinjer 1
 
0.8%
mary 1
 
0.8%
Other values (104) 104
86.7%
2025-05-05T05:32:43.011520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 115
 
12.7%
70
 
7.7%
e 62
 
6.8%
a 53
 
5.8%
[ 50
 
5.5%
] 50
 
5.5%
n 43
 
4.7%
i 40
 
4.4%
o 33
 
3.6%
r 32
 
3.5%
Other values (49) 358
39.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 115
 
12.7%
70
 
7.7%
e 62
 
6.8%
a 53
 
5.8%
[ 50
 
5.5%
] 50
 
5.5%
n 43
 
4.7%
i 40
 
4.4%
o 33
 
3.6%
r 32
 
3.5%
Other values (49) 358
39.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 115
 
12.7%
70
 
7.7%
e 62
 
6.8%
a 53
 
5.8%
[ 50
 
5.5%
] 50
 
5.5%
n 43
 
4.7%
i 40
 
4.4%
o 33
 
3.6%
r 32
 
3.5%
Other values (49) 358
39.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 115
 
12.7%
70
 
7.7%
e 62
 
6.8%
a 53
 
5.8%
[ 50
 
5.5%
] 50
 
5.5%
n 43
 
4.7%
i 40
 
4.4%
o 33
 
3.6%
r 32
 
3.5%
Other values (49) 358
39.5%

danceability
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58276
Minimum0.137
Maximum0.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:43.409420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.137
5-th percentile0.33645
Q10.417
median0.584
Q30.75775
95-th percentile0.86
Maximum0.94
Range0.803
Interquartile range (IQR)0.34075

Descriptive statistics

Standard deviation0.19480336
Coefficient of variation (CV)0.33427717
Kurtosis-0.8158478
Mean0.58276
Median Absolute Deviation (MAD)0.1735
Skewness-0.17950754
Sum29.138
Variance0.037948349
MonotonicityNot monotonic
2025-05-05T05:32:43.921112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.364 2
 
4.0%
0.746 1
 
2.0%
0.613 1
 
2.0%
0.36 1
 
2.0%
0.794 1
 
2.0%
0.548 1
 
2.0%
0.705 1
 
2.0%
0.353 1
 
2.0%
0.648 1
 
2.0%
0.327 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
0.137 1
2.0%
0.181 1
2.0%
0.327 1
2.0%
0.348 1
2.0%
0.353 1
2.0%
0.359 1
2.0%
0.36 1
2.0%
0.364 2
4.0%
0.365 1
2.0%
0.397 1
2.0%
ValueCountFrequency (%)
0.94 1
2.0%
0.88 1
2.0%
0.878 1
2.0%
0.838 1
2.0%
0.818 1
2.0%
0.814 1
2.0%
0.805 1
2.0%
0.803 1
2.0%
0.796 1
2.0%
0.794 1
2.0%

duration_ms
Real number (ℝ)

High correlation  Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242780.58
Minimum48480
Maximum592920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:44.365183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum48480
5-th percentile163753.45
Q1200486.75
median223597
Q3266153.25
95-th percentile344061
Maximum592920
Range544440
Interquartile range (IQR)65666.5

Descriptive statistics

Standard deviation81564.666
Coefficient of variation (CV)0.33596042
Kurtosis6.7791164
Mean242780.58
Median Absolute Deviation (MAD)32686.5
Skewness1.7739354
Sum12139029
Variance6.6527947 × 109
MonotonicityNot monotonic
2025-05-05T05:32:44.790695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
305840 1
 
2.0%
220600 1
 
2.0%
252093 1
 
2.0%
262053 1
 
2.0%
226154 1
 
2.0%
205915 1
 
2.0%
228733 1
 
2.0%
355653 1
 
2.0%
329893 1
 
2.0%
207520 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
48480 1
2.0%
141293 1
2.0%
157606 1
2.0%
171267 1
2.0%
172507 1
2.0%
178080 1
2.0%
178493 1
2.0%
180000 1
2.0%
186440 1
2.0%
188120 1
2.0%
ValueCountFrequency (%)
592920 1
2.0%
453987 1
2.0%
355653 1
2.0%
329893 1
2.0%
327093 1
2.0%
314320 1
2.0%
309760 1
2.0%
307347 1
2.0%
305840 1
2.0%
305571 1
2.0%

duration_minutes
Real number (ℝ)

High correlation  Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.046343
Minimum0.808
Maximum9.882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:45.215915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.808
5-th percentile2.7292242
Q13.3414458
median3.7266167
Q34.4358875
95-th percentile5.73435
Maximum9.882
Range9.074
Interquartile range (IQR)1.0944417

Descriptive statistics

Standard deviation1.3594111
Coefficient of variation (CV)0.33596042
Kurtosis6.7791164
Mean4.046343
Median Absolute Deviation (MAD)0.544775
Skewness1.7739354
Sum202.31715
Variance1.8479985
MonotonicityNot monotonic
2025-05-05T05:32:45.966178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.097333333 1
 
2.0%
3.676666667 1
 
2.0%
4.20155 1
 
2.0%
4.36755 1
 
2.0%
3.769233333 1
 
2.0%
3.431916667 1
 
2.0%
3.812216667 1
 
2.0%
5.92755 1
 
2.0%
5.498216667 1
 
2.0%
3.458666667 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
0.808 1
2.0%
2.354883333 1
2.0%
2.626766667 1
2.0%
2.85445 1
2.0%
2.875116667 1
2.0%
2.968 1
2.0%
2.974883333 1
2.0%
3 1
2.0%
3.107333333 1
2.0%
3.135333333 1
2.0%
ValueCountFrequency (%)
9.882 1
2.0%
7.56645 1
2.0%
5.92755 1
2.0%
5.498216667 1
2.0%
5.45155 1
2.0%
5.238666667 1
2.0%
5.162666667 1
2.0%
5.12245 1
2.0%
5.097333333 1
2.0%
5.09285 1
2.0%

energy
Real number (ℝ)

High correlation  Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63806
Minimum0.104
Maximum0.976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:46.469214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.104
5-th percentile0.2125
Q10.44625
median0.6825
Q30.836
95-th percentile0.95615
Maximum0.976
Range0.872
Interquartile range (IQR)0.38975

Descriptive statistics

Standard deviation0.24360728
Coefficient of variation (CV)0.38179369
Kurtosis-0.6930285
Mean0.63806
Median Absolute Deviation (MAD)0.1725
Skewness-0.54962718
Sum31.903
Variance0.059344507
MonotonicityNot monotonic
2025-05-05T05:32:46.927439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.841 1
 
2.0%
0.313 1
 
2.0%
0.778 1
 
2.0%
0.333 1
 
2.0%
0.524 1
 
2.0%
0.938 1
 
2.0%
0.669 1
 
2.0%
0.742 1
 
2.0%
0.352 1
 
2.0%
0.104 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
0.104 1
2.0%
0.143 1
2.0%
0.208 1
2.0%
0.218 1
2.0%
0.237 1
2.0%
0.245 1
2.0%
0.313 1
2.0%
0.333 1
2.0%
0.352 1
2.0%
0.405 1
2.0%
ValueCountFrequency (%)
0.976 1
2.0%
0.975 1
2.0%
0.971 1
2.0%
0.938 1
2.0%
0.934 1
2.0%
0.921 1
2.0%
0.917 1
2.0%
0.904 1
2.0%
0.899 1
2.0%
0.874 1
2.0%

explicit
Categorical

High correlation 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0.0
38 
1.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters150
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 38
76.0%
1.0 12
 
24.0%

Length

2025-05-05T05:32:47.227811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T05:32:47.438591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 38
76.0%
1.0 12
 
24.0%

Most occurring characters

ValueCountFrequency (%)
0 88
58.7%
. 50
33.3%
1 12
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 88
58.7%
. 50
33.3%
1 12
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 88
58.7%
. 50
33.3%
1 12
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 88
58.7%
. 50
33.3%
1 12
 
8.0%

id
Text

Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2025-05-05T05:32:47.995707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1100
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row5eEj1vkMIqtmvaTk7cS4UC
2nd row0Cyui3k9S61bue3uMkBD9S
3rd row71KtwiEpzcn9WxwlaS7ZJY
4th row7tf64lNC31lWlTsih0nfZf
5th row4Mpm64J9yqluKaOssgVQ9P
ValueCountFrequency (%)
5eej1vkmiqtmvatk7cs4uc 1
 
2.0%
1kdpwuua213r6m2wbwuaue 1
 
2.0%
6rkay9rk1ntfb94qxg3ljr 1
 
2.0%
71ktwiepzcn9wxwlas7zjy 1
 
2.0%
7tf64lnc31lwltsih0nfzf 1
 
2.0%
4mpm64j9yqlukaossgvq9p 1
 
2.0%
75e1eyhlzb3mqzqbcrmkln 1
 
2.0%
2s9nvnrau9wahywra0ou9m 1
 
2.0%
32c5of1paeu7ieebgilwao 1
 
2.0%
7racifpvu5vuhmqvbw2c1h 1
 
2.0%
Other values (40) 40
80.0%
2025-05-05T05:32:48.804125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 31
 
2.8%
7 28
 
2.5%
0 27
 
2.5%
4 26
 
2.4%
3 25
 
2.3%
2 23
 
2.1%
F 23
 
2.1%
U 23
 
2.1%
r 22
 
2.0%
E 22
 
2.0%
Other values (52) 850
77.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 31
 
2.8%
7 28
 
2.5%
0 27
 
2.5%
4 26
 
2.4%
3 25
 
2.3%
2 23
 
2.1%
F 23
 
2.1%
U 23
 
2.1%
r 22
 
2.0%
E 22
 
2.0%
Other values (52) 850
77.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 31
 
2.8%
7 28
 
2.5%
0 27
 
2.5%
4 26
 
2.4%
3 25
 
2.3%
2 23
 
2.1%
F 23
 
2.1%
U 23
 
2.1%
r 22
 
2.0%
E 22
 
2.0%
Other values (52) 850
77.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 31
 
2.8%
7 28
 
2.5%
0 27
 
2.5%
4 26
 
2.4%
3 25
 
2.3%
2 23
 
2.1%
F 23
 
2.1%
U 23
 
2.1%
r 22
 
2.0%
E 22
 
2.0%
Other values (52) 850
77.3%

instrumentalness
Real number (ℝ)

Zeros 

Distinct35
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.065135326
Minimum0
Maximum0.912
Zeros16
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:50.105164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.815 × 10-5
Q30.00050975
95-th percentile0.53605
Maximum0.912
Range0.912
Interquartile range (IQR)0.00050975

Descriptive statistics

Standard deviation0.2085659
Coefficient of variation (CV)3.2020396
Kurtosis11.800426
Mean0.065135326
Median Absolute Deviation (MAD)1.815 × 10-5
Skewness3.5587258
Sum3.2567663
Variance0.043499733
MonotonicityNot monotonic
2025-05-05T05:32:50.371675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 16
32.0%
7.4 × 10-51
 
2.0%
3.41 × 10-61
 
2.0%
0.266 1
 
2.0%
0.912 1
 
2.0%
1.02 × 10-51
 
2.0%
0.000257 1
 
2.0%
0.000123 1
 
2.0%
0.219 1
 
2.0%
1.09 × 10-61
 
2.0%
Other values (25) 25
50.0%
ValueCountFrequency (%)
0 16
32.0%
1.09 × 10-61
 
2.0%
3.37 × 10-61
 
2.0%
3.41 × 10-61
 
2.0%
4.38 × 10-61
 
2.0%
4.43 × 10-61
 
2.0%
4.95 × 10-61
 
2.0%
4.98 × 10-61
 
2.0%
1.02 × 10-51
 
2.0%
1.08 × 10-51
 
2.0%
ValueCountFrequency (%)
0.912 1
2.0%
0.899 1
2.0%
0.757 1
2.0%
0.266 1
2.0%
0.219 1
2.0%
0.085 1
2.0%
0.0586 1
2.0%
0.027 1
2.0%
0.0218 1
2.0%
0.00613 1
2.0%

key
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.88
Minimum0
Maximum11
Zeros2
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:50.627823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.25
median7.5
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation3.2365262
Coefficient of variation (CV)0.47042532
Kurtosis-0.4469564
Mean6.88
Median Absolute Deviation (MAD)2
Skewness-0.70048703
Sum344
Variance10.475102
MonotonicityNot monotonic
2025-05-05T05:32:50.890039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 7
14.0%
10 6
12.0%
11 6
12.0%
9 6
12.0%
7 6
12.0%
6 6
12.0%
1 4
8.0%
4 3
6.0%
0 2
 
4.0%
5 2
 
4.0%
ValueCountFrequency (%)
0 2
 
4.0%
1 4
8.0%
2 2
 
4.0%
4 3
6.0%
5 2
 
4.0%
6 6
12.0%
7 6
12.0%
8 7
14.0%
9 6
12.0%
10 6
12.0%
ValueCountFrequency (%)
11 6
12.0%
10 6
12.0%
9 6
12.0%
8 7
14.0%
7 6
12.0%
6 6
12.0%
5 2
 
4.0%
4 3
6.0%
2 2
 
4.0%
1 4
8.0%

liveness
Real number (ℝ)

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.176412
Minimum0.0533
Maximum0.791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:51.189439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0533
5-th percentile0.06828
Q10.094475
median0.1135
Q30.2305
95-th percentile0.35465
Maximum0.791
Range0.7377
Interquartile range (IQR)0.136025

Descriptive statistics

Standard deviation0.13061574
Coefficient of variation (CV)0.74040166
Kurtosis8.8344642
Mean0.176412
Median Absolute Deviation (MAD)0.0353
Skewness2.4406964
Sum8.8206
Variance0.017060471
MonotonicityNot monotonic
2025-05-05T05:32:51.587657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.109 2
 
4.0%
0.106 2
 
4.0%
0.242 1
 
2.0%
0.268 1
 
2.0%
0.356 1
 
2.0%
0.114 1
 
2.0%
0.11 1
 
2.0%
0.353 1
 
2.0%
0.0966 1
 
2.0%
0.0846 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.0533 1
2.0%
0.0629 1
2.0%
0.0681 1
2.0%
0.0685 1
2.0%
0.0745 1
2.0%
0.0819 1
2.0%
0.0821 1
2.0%
0.0846 1
2.0%
0.087 1
2.0%
0.0891 1
2.0%
ValueCountFrequency (%)
0.791 1
2.0%
0.407 1
2.0%
0.356 1
2.0%
0.353 1
2.0%
0.351 1
2.0%
0.328 1
2.0%
0.319 1
2.0%
0.309 1
2.0%
0.294 1
2.0%
0.275 1
2.0%

loudness
Real number (ℝ)

High correlation  Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.99416
Minimum-19.801
Maximum1.073
Zeros0
Zeros (%)0.0%
Negative49
Negative (%)98.0%
Memory size532.0 B
2025-05-05T05:32:52.040306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-19.801
5-th percentile-15.1459
Q1-8.342
median-6.108
Q3-4.6335
95-th percentile-2.63355
Maximum1.073
Range20.874
Interquartile range (IQR)3.7085

Descriptive statistics

Standard deviation3.8138427
Coefficient of variation (CV)-0.5452896
Kurtosis2.3788949
Mean-6.99416
Median Absolute Deviation (MAD)1.6215
Skewness-1.2458259
Sum-349.708
Variance14.545396
MonotonicityNot monotonic
2025-05-05T05:32:52.363039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.831 1
 
2.0%
-11.379 1
 
2.0%
-3.933 1
 
2.0%
-8.491 1
 
2.0%
-7.625 1
 
2.0%
-4.073 1
 
2.0%
-5.974 1
 
2.0%
-7.389 1
 
2.0%
-10.11 1
 
2.0%
-19.801 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
-19.801 1
2.0%
-16.273 1
2.0%
-15.235 1
2.0%
-15.037 1
2.0%
-12.333 1
2.0%
-11.379 1
2.0%
-10.47 1
2.0%
-10.11 1
2.0%
-9.835 1
2.0%
-9.497 1
2.0%
ValueCountFrequency (%)
1.073 1
2.0%
-1.892 1
2.0%
-2.238 1
2.0%
-3.117 1
2.0%
-3.322 1
2.0%
-3.903 1
2.0%
-3.924 1
2.0%
-3.933 1
2.0%
-4.073 1
2.0%
-4.258 1
2.0%

mode
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
1.0
34 
0.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters150
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 34
68.0%
0.0 16
32.0%

Length

2025-05-05T05:32:52.658046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-05T05:32:52.861945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 34
68.0%
0.0 16
32.0%

Most occurring characters

ValueCountFrequency (%)
0 66
44.0%
. 50
33.3%
1 34
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 66
44.0%
. 50
33.3%
1 34
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 66
44.0%
. 50
33.3%
1 34
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 66
44.0%
. 50
33.3%
1 34
22.7%

name
Text

Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2025-05-05T05:32:53.688548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length38
Median length24
Mean length15.64
Min length5

Characters and Unicode

Total characters782
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowShe Will
2nd rowGirls Like U
3rd rowEnjoy the Silence - cover version
4th rowGood Vibrations
5th rowCall Me
ValueCountFrequency (%)
the 8
 
5.1%
you 4
 
2.6%
hush 4
 
2.6%
4
 
2.6%
with 3
 
1.9%
i'm 3
 
1.9%
me 3
 
1.9%
she 2
 
1.3%
silence 2
 
1.3%
i 2
 
1.3%
Other values (115) 121
77.6%
2025-05-05T05:32:54.816823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
106
 
13.6%
e 64
 
8.2%
o 50
 
6.4%
a 47
 
6.0%
i 45
 
5.8%
n 38
 
4.9%
t 34
 
4.3%
r 27
 
3.5%
s 27
 
3.5%
l 24
 
3.1%
Other values (47) 320
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
106
 
13.6%
e 64
 
8.2%
o 50
 
6.4%
a 47
 
6.0%
i 45
 
5.8%
n 38
 
4.9%
t 34
 
4.3%
r 27
 
3.5%
s 27
 
3.5%
l 24
 
3.1%
Other values (47) 320
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
106
 
13.6%
e 64
 
8.2%
o 50
 
6.4%
a 47
 
6.0%
i 45
 
5.8%
n 38
 
4.9%
t 34
 
4.3%
r 27
 
3.5%
s 27
 
3.5%
l 24
 
3.1%
Other values (47) 320
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
106
 
13.6%
e 64
 
8.2%
o 50
 
6.4%
a 47
 
6.0%
i 45
 
5.8%
n 38
 
4.9%
t 34
 
4.3%
r 27
 
3.5%
s 27
 
3.5%
l 24
 
3.1%
Other values (47) 320
40.9%

popularity
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.62
Minimum36
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:55.064980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile40
Q145.25
median53
Q359
95-th percentile68.55
Maximum90
Range54
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation10.470347
Coefficient of variation (CV)0.19526943
Kurtosis1.9571043
Mean53.62
Median Absolute Deviation (MAD)7
Skewness1.0033142
Sum2681
Variance109.62816
MonotonicityNot monotonic
2025-05-05T05:32:55.358030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
44 4
 
8.0%
50 3
 
6.0%
40 3
 
6.0%
61 3
 
6.0%
59 3
 
6.0%
51 2
 
4.0%
48 2
 
4.0%
53 2
 
4.0%
54 2
 
4.0%
41 2
 
4.0%
Other values (19) 24
48.0%
ValueCountFrequency (%)
36 1
 
2.0%
40 3
6.0%
41 2
4.0%
42 1
 
2.0%
43 1
 
2.0%
44 4
8.0%
45 1
 
2.0%
46 1
 
2.0%
48 2
4.0%
49 2
4.0%
ValueCountFrequency (%)
90 1
 
2.0%
79 1
 
2.0%
69 1
 
2.0%
68 2
4.0%
65 1
 
2.0%
64 1
 
2.0%
62 1
 
2.0%
61 3
6.0%
60 1
 
2.0%
59 3
6.0%
Distinct44
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
Minimum2000-01-01 00:00:00
Maximum2020-06-12 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-05T05:32:55.639404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:56.005841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)

speechiness
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084392
Minimum0.0262
Maximum0.411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:56.379459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0262
5-th percentile0.0285
Q10.038975
median0.0471
Q30.09395
95-th percentile0.28055
Maximum0.411
Range0.3848
Interquartile range (IQR)0.054975

Descriptive statistics

Standard deviation0.08179838
Coefficient of variation (CV)0.969267
Kurtosis5.6103791
Mean0.084392
Median Absolute Deviation (MAD)0.01565
Skewness2.3429221
Sum4.2196
Variance0.006690975
MonotonicityNot monotonic
2025-05-05T05:32:56.832246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0285 2
 
4.0%
0.119 1
 
2.0%
0.0902 1
 
2.0%
0.0689 1
 
2.0%
0.115 1
 
2.0%
0.0535 1
 
2.0%
0.0262 1
 
2.0%
0.0393 1
 
2.0%
0.0278 1
 
2.0%
0.0371 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
0.0262 1
2.0%
0.0278 1
2.0%
0.0285 2
4.0%
0.0302 1
2.0%
0.0303 1
2.0%
0.0324 1
2.0%
0.0333 1
2.0%
0.0357 1
2.0%
0.0359 1
2.0%
0.0371 1
2.0%
ValueCountFrequency (%)
0.411 1
2.0%
0.304 1
2.0%
0.281 1
2.0%
0.28 1
2.0%
0.203 1
2.0%
0.17 1
2.0%
0.146 1
2.0%
0.134 1
2.0%
0.122 1
2.0%
0.119 1
2.0%

tempo
Real number (ℝ)

Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.79384
Minimum74.53
Maximum209.793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:57.333290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum74.53
5-th percentile78.04635
Q198.5605
median118.775
Q3138.43975
95-th percentile170.1033
Maximum209.793
Range135.263
Interquartile range (IQR)39.87925

Descriptive statistics

Standard deviation29.16558
Coefficient of variation (CV)0.24144924
Kurtosis0.63832785
Mean120.79384
Median Absolute Deviation (MAD)20.9385
Skewness0.59665998
Sum6039.692
Variance850.63108
MonotonicityNot monotonic
2025-05-05T05:32:57.825976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.14 1
 
2.0%
82.004 1
 
2.0%
129.483 1
 
2.0%
74.53 1
 
2.0%
130.122 1
 
2.0%
155.038 1
 
2.0%
118.539 1
 
2.0%
79.033 1
 
2.0%
121.459 1
 
2.0%
97.279 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
74.53 1
2.0%
76.309 1
2.0%
78.009 1
2.0%
78.092 1
2.0%
79.033 1
2.0%
82.004 1
2.0%
88.377 1
2.0%
89.706 1
2.0%
89.989 1
2.0%
89.995 1
2.0%
ValueCountFrequency (%)
209.793 1
2.0%
180.068 1
2.0%
180.006 1
2.0%
158 1
2.0%
155.696 1
2.0%
155.038 1
2.0%
150.974 1
2.0%
145.166 1
2.0%
144.605 1
2.0%
142.998 1
2.0%

valence
Real number (ℝ)

High correlation  Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.489166
Minimum0.0366
Maximum0.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:58.300927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0366
5-th percentile0.1091
Q10.27075
median0.5025
Q30.69925
95-th percentile0.94205
Maximum0.96
Range0.9234
Interquartile range (IQR)0.4285

Descriptive statistics

Standard deviation0.2675219
Coefficient of variation (CV)0.5468939
Kurtosis-1.0463668
Mean0.489166
Median Absolute Deviation (MAD)0.2185
Skewness0.13993097
Sum24.4583
Variance0.071567969
MonotonicityNot monotonic
2025-05-05T05:32:58.764568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0837 1
 
2.0%
0.356 1
 
2.0%
0.626 1
 
2.0%
0.17 1
 
2.0%
0.604 1
 
2.0%
0.744 1
 
2.0%
0.673 1
 
2.0%
0.166 1
 
2.0%
0.173 1
 
2.0%
0.119 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
0.0366 1
2.0%
0.0837 1
2.0%
0.101 1
2.0%
0.119 1
2.0%
0.133 1
2.0%
0.148 1
2.0%
0.15 1
2.0%
0.166 1
2.0%
0.17 1
2.0%
0.173 1
2.0%
ValueCountFrequency (%)
0.96 1
2.0%
0.948 1
2.0%
0.947 1
2.0%
0.936 1
2.0%
0.884 1
2.0%
0.864 1
2.0%
0.851 1
2.0%
0.819 1
2.0%
0.791 1
2.0%
0.784 1
2.0%

year
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.1
Minimum2000
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2025-05-05T05:32:59.183237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000.45
Q12004
median2009
Q32015
95-th percentile2019
Maximum2020
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.2507142
Coefficient of variation (CV)0.0031112012
Kurtosis-1.2561891
Mean2009.1
Median Absolute Deviation (MAD)5.5
Skewness0.15882228
Sum100455
Variance39.071429
MonotonicityNot monotonic
2025-05-05T05:32:59.584108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2016 5
10.0%
2009 5
10.0%
2006 4
 
8.0%
2004 4
 
8.0%
2001 4
 
8.0%
2002 4
 
8.0%
2005 3
 
6.0%
2015 3
 
6.0%
2000 3
 
6.0%
2010 3
 
6.0%
Other values (6) 12
24.0%
ValueCountFrequency (%)
2000 3
6.0%
2001 4
8.0%
2002 4
8.0%
2004 4
8.0%
2005 3
6.0%
2006 4
8.0%
2009 5
10.0%
2010 3
6.0%
2011 2
 
4.0%
2012 2
 
4.0%
ValueCountFrequency (%)
2020 2
 
4.0%
2019 2
 
4.0%
2018 2
 
4.0%
2016 5
10.0%
2015 3
6.0%
2014 2
 
4.0%
2012 2
 
4.0%
2011 2
 
4.0%
2010 3
6.0%
2009 5
10.0%

Interactions

2025-05-05T05:32:34.849784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:44.585229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:47.359095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:50.596077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:55.259496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:58.756517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:02.908191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:07.092192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:13.157313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:16.826405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:19.410022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:22.049422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:25.652284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:29.974696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:35.065728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:44.841802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:47.579627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:50.801442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:55.478770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:58.939535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:03.253973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:07.333680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:13.432163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:16.995942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:19.575155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:22.229113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:25.921261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:30.212001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:35.282376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:45.072046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:47.835282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:51.051176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:55.867308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:59.165476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:03.735151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:07.617371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:13.717824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:17.202331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:19.787852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:22.449177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:26.269640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:30.597840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:35.544123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:45.287551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:48.065160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:51.306109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:56.092086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:59.382054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:04.105628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:09.117396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:13.998189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:17.379230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:19.971231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:22.649383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:26.600656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:30.893581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:35.757873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:45.463019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:48.275083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:51.605751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:56.258776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:59.562527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:04.445225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:09.383651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:14.285136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:17.549497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:20.140780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:22.829931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:26.856208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:31.349404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:35.951802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:45.658035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:48.489186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:52.185506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:56.517281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:59.725348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:04.763312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:09.700489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:14.587818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:17.754078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:20.309489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:23.017059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:27.103152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:31.735042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:36.171896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:45.860914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:48.714885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:52.592973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:56.730308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:59.924947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:04.986244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:10.034305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:14.961187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:17.926215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:20.504511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:23.227963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:27.358370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:32.121540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:36.402283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:46.039421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:48.984822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:52.928664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:56.969262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:00.167840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:05.234051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:10.368529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:15.315964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:18.094892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:20.710567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:23.470457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:27.732069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:32.567092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:36.674276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:46.226785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:49.202039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:53.277861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:57.294935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:00.451045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:05.618627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:10.750463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:15.618969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:18.286875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:20.906508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:23.745669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:28.107939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:32.976296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:36.919898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:46.391322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:49.428126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:53.637051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:57.522406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:00.684398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:05.833701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:11.084846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:15.818323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:18.444993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:21.075082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:23.989280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:28.460302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:33.315053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:37.129729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:46.595339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:49.651108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:53.941249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:57.782796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:00.984347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:06.067204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:11.448362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:16.009689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:18.626390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:21.250418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:24.318481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:28.790263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:33.766645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:37.360273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:46.806071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:49.921349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:54.296455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:58.096641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:01.356498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:06.300583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:11.967288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:16.221338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:18.823214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:21.451382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:24.696534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:29.073202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:34.151399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:37.594091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:46.989495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:50.134540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:54.573097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:58.332495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:01.855206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:06.584043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:12.358510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:16.418258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:18.997186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:21.637846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:24.994280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:29.375263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:34.367842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:37.826091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:47.158031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:50.317867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:54.892187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:31:58.550487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:02.314680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:06.833931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:12.723228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:16.607599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:19.190307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:21.840055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:25.312335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:29.665687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-05T05:32:34.583530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-05-05T05:32:59.905774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
acousticnessdanceabilityduration_minutesduration_msenergyexplicitinstrumentalnesskeylivenessloudnessmodepopularityspeechinesstempovalenceyear
acousticness1.0000.016-0.050-0.050-0.7340.0000.2170.051-0.182-0.5520.000-0.126-0.354-0.0440.0110.012
danceability0.0161.000-0.059-0.0590.0220.000-0.0570.184-0.0190.1410.0000.1970.3180.0050.619-0.009
duration_minutes-0.050-0.0591.0001.000-0.1600.252-0.000-0.0250.086-0.0330.0000.008-0.007-0.486-0.2680.034
duration_ms-0.050-0.0591.0001.000-0.1600.252-0.000-0.0250.086-0.0330.0000.008-0.007-0.486-0.2680.034
energy-0.7340.022-0.160-0.1601.0000.000-0.323-0.0360.0930.8020.0000.0360.2350.1120.288-0.081
explicit0.0000.0000.2520.2520.0001.0000.0000.2250.2950.0000.0000.0000.6170.2880.0000.000
instrumentalness0.217-0.057-0.000-0.000-0.3230.0001.000-0.020-0.051-0.3820.0000.046-0.1940.047-0.3510.124
key0.0510.184-0.025-0.025-0.0360.225-0.0201.0000.1850.0590.3340.134-0.0540.0240.1860.091
liveness-0.182-0.0190.0860.0860.0930.295-0.0510.1851.0000.1590.000-0.0800.1780.0550.104-0.299
loudness-0.5520.141-0.033-0.0330.8020.000-0.3820.0590.1591.0000.0000.1040.3270.0620.398-0.060
mode0.0000.0000.0000.0000.0000.0000.0000.3340.0000.0001.0000.2960.1090.0000.0000.000
popularity-0.1260.1970.0080.0080.0360.0000.0460.134-0.0800.1040.2961.000-0.0170.2310.1840.624
speechiness-0.3540.318-0.007-0.0070.2350.617-0.194-0.0540.1780.3270.109-0.0171.000-0.0270.2040.075
tempo-0.0440.005-0.486-0.4860.1120.2880.0470.0240.0550.0620.0000.231-0.0271.0000.0560.339
valence0.0110.619-0.268-0.2680.2880.000-0.3510.1860.1040.3980.0000.1840.2040.0561.000-0.146
year0.012-0.0090.0340.034-0.0810.0000.1240.091-0.299-0.0600.0000.6240.0750.339-0.1461.000

Missing values

2025-05-05T05:32:38.656380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-05T05:32:39.408396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

acousticnessartistsdanceabilityduration_msduration_minutesenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempovalenceyear
00.000402['Lil Wayne', 'Drake']0.364305840.05.0973330.8411.05eEj1vkMIqtmvaTk7cS4UC0.0000008.00.2420-4.8311.0She Will51.02011-01-010.1190100.1400.08372011.0
10.310000['blackbear']0.758180000.03.0000000.4271.00Cyui3k9S61bue3uMkBD9S0.00000010.00.1150-9.8350.0Girls Like U56.02016-06-170.1130139.9110.48902016.0
20.000984['Lacuna Coil']0.503245573.04.0928830.6670.071KtwiEpzcn9WxwlaS7ZJY0.02700011.00.2290-6.8310.0Enjoy the Silence - cover version55.020060.0285113.0180.10102006.0
30.334000['The Beach Boys']0.414215827.03.5971170.4240.07tf64lNC31lWlTsih0nfZf0.00000510.00.1310-9.4971.0Good Vibrations46.02012-01-010.0399132.4300.32502012.0
40.001700['In This Moment']0.426197627.03.2937830.9750.04Mpm64J9yqluKaOssgVQ9P0.0000000.00.0891-1.8921.0Call Me50.02009-04-100.0950142.9980.27302009.0
50.866000['Anna Kendrick']0.39748480.00.8080000.2180.075e1EYhLzB3mQZQBcRmklN0.0000259.00.0819-10.4701.0The Sound Of Silence57.02016-09-230.0403209.7930.56702016.0
60.006890['Boosie Badazz', 'Webbie', 'Bun B']0.675267520.04.4586670.7811.02s9NvnrAU9WahyWRa0Ou9m0.0000008.00.3190-6.0351.0Give Me That44.02005-05-170.280078.0090.53602005.0
70.095600['Jake Owen']0.672172507.02.8751170.9340.032C5of1pAeU7IeEbGiLWAo0.0000007.00.1090-3.3221.0Yee Haw49.02006-07-010.0333129.1230.94802006.0
80.650000['Peter, Paul and Mary']0.581208560.03.4760000.1430.07raciFPVU5VuHmqVbw2c1h0.0000009.00.1040-16.2731.0Puff, the Magic Dragon - 2004 Remaster52.02005-08-230.0401144.6050.46902005.0
90.017400['Jinjer']0.600305571.05.0928500.8490.01XT5VDdxwI2Bx01MxURMcz0.0020509.00.0745-5.7880.0Pisces50.02016-07-290.0376140.0020.25702016.0
acousticnessartistsdanceabilityduration_msduration_minutesenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempovalenceyear
400.007270["Destiny's Child"]0.814242013.04.0335500.8990.04dvQg9sD8k9y4qiEURuj8v0.2190001.00.0979-5.9581.0Lose My Breath57.02004-11-160.0637119.0110.5452004.0
410.018500['Wu-Tang Clan']0.803233640.03.8940000.8681.02YfYTyShcbMFFWaZQXKb5V0.0000006.00.0821-3.9031.0Rules45.02001-12-180.304095.5140.9362001.0
420.014600['Eminem']0.878309760.05.1626670.5951.03xVtc7PB4V3AqAfq4Iahli0.00006811.00.3280-3.9240.0I'm Back59.02000-05-230.203089.9950.8512000.0
430.065600['Third Day']0.364199760.03.3293330.6540.00P5qOLfYWTL1tEzCkTCjbE0.0000002.00.2010-6.6481.0Show Me Your Glory40.020010.0359158.0000.2412001.0
440.031800['6ix9ine', 'Nicki Minaj']0.762202667.03.3777830.4230.04HreNRemFIo8zi38IGFl7U0.0005161.00.1090-8.6491.0TROLLZ (with Nicki Minaj)68.02020-06-120.2810180.0680.4302020.0
450.177000['Regard']0.880157606.02.6267670.7510.02tnVG71enUj33Ic2nFN6kZ0.0000647.00.1060-4.2580.0Ride It90.02019-07-260.0874117.9480.8842019.0
460.000005['Five Finger Death Punch']0.513209493.03.4915500.9761.000cP5rqzL6ZrwMaqTEeGIN0.00034011.00.1170-3.1171.0Hard To See53.02009-09-220.1700111.9920.1332009.0
470.562000['Deitrick Haddon']0.757302000.05.0333330.4050.01I3v23MpvlCQQ7ugP7jkQ00.0000048.00.2750-6.1501.0Sinner's Prayer41.020020.0908134.0260.3822002.0
480.445000['Best Coast']0.137178080.02.9680000.8210.07Cb9fYGYtFaANCaqYEOCDC0.7570007.00.3090-4.5901.0When I'm With You49.02010-06-260.0548145.1660.3062010.0
490.210000['The xx']0.940188120.03.1353330.2080.04ORbUYpR6MDsJYTHh3xBWH0.0850009.00.1050-15.2350.0Basic Space44.02009-08-180.0853115.8720.1482009.0